A version-aware computation and storage trade-off strategy for multi-version VoD systems in the cloud

Nowdays, many Video-on-Demand (VoD) providers offer multiple-quality video streaming services to heterogeneous clients, called as multi-version VoD. Some researches focus on video transcoding in real-time or video layered encoding/decoding, but they are not widely used in VoD industry. Storing multiple versions of the same video is an easy solution, but it consumes lots of storage space. Although there are also a few works about trading-off between transcoding and storage, they did not utilize the transcoding relationships among different versions and took the video popularity into account, which bring that they may have little cost-efficiency for multi-version VoD systems. To minimize the cost, in this paper, we propose a version-aware transcoding computation and storage trade-off strategy for multi-version VoD systems in the cloud. Firstly, it utilizes the transcoding weight graph to describe the transcoding relationships among different versions of a video. According to the graph, the transcoding computation cost from one version to another version can be calculated. Secondly, it takes the video popularity of different versions, the prices of storage and computation resources in the cloud into account to decide which versions of which videos should be stored or transcoded. We then formulate it as an optimization problem and present a heuristic approximate optimal solution. Finally, we conduct extensive simulations to evaluate our strategy and solution, and the results show that they can significantly lower the cost of multi-version VoD systems.